Gender: Female 
Age: 25-34
Nationality: United Kingdom
Ethnicity: Black or African American 
Marital status: Married 
First language: English 
Religion: Muslim 
Education: Undergraduate degree
Mother's education: Undergraduate degree
Father's education: Undergraduate degree
Self-assessed socioeconomic status (1-10): 7
Home: Rent 
Employment: Employed full time
Occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Mother's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Father's occupation: Professionals or highly skilled workers (those who increase the existing stock of knowledge or teach about it in a systematic manner, e.g. engineer, doctor, teacher, lawyer)
Hobbies: Use social media;Go to gigs;Attend football matches;Exercise 
Devices used daily: Laptop;Tablet;Smartphone 
NLP tools known: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Grammar checker (i.e., correcting grammar mistakes);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Spell checker (i.e., correcting misspellings) 
NLP tools used: Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Question Answering & Search Engine. (e.g., browsers or ask questions to smart devices/assistants);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
Frequency of LLM use: Every day
LLMs used: Character.AI;ChatGPT;Claude;GitHub Copilot;Google Bard;Google Gemini;Snapchat My AI 
LLM use cases: Coding;Translation;Writing (e.g., composing emails);Digital content creation (e.g., creation of social media content);Paraphrasing or finding synonyms;Solving math or logical problems;Generic chatbot conversation;Analyze data 
Contexts of LLM use: Work;Technical;Learning;Entertainment 
Prompt 1: export const getProducts = async (req: AuthenticatedRequest, res: Response) => {   try {     const { storeId: sId, query } = req;     const {       q,       sort = "default",       category,       minPrice,       maxPrice,       size = 20,       storeId: _sId,       productsToShow,       colors, // Array of color names       sizes, // Array of sizes       gender, // Gender filter       rating, // Minimum rating       isActive,     } = query as unknown as {       q?: string;       sort?: "default" | "stock-level" | "low-to-high" | "high-to-low";       category?: string;       minPrice?: string;       maxPrice?: string;       size?: number;       storeId: string;       productsToShow?: string;       colors?: string[];       sizes?: string[];       gender?: IGender[];       rating?: number;       isActive?: boolean;     };      const storeId = sId || _sId;     let matchStage: any = { storeId };      // Basic filters     if (q) {       matchStage.$or = [         { productName: { $regex: q, $options: "i" } },         { description: { $regex: q, $options: "i" } },         { tags: { $regex: q, $options: "i" } },       ];     }      if (category) {       matchStage.category = category;     }      if (typeof isActive === "boolean") {       matchStage.isActive = isActive;     }      // Color filter - using availableColors array     if (colors && colors.length > 0) {       matchStage["availableColors.name"] = { $in: colors };     }      // Size filter - using availableSizes array     if (sizes && sizes.length > 0) {       matchStage.availableSizes = { $in: sizes };     }      // Gender filter     if (gender && gender.length > 0) {       matchStage.gender = { $in: gender };     }      // Rating filter - using ratings.average     if (rating) {       matchStage["ratings.average"] = { $gte: Number(rating) };     }      // Price range filter     if (minPrice || maxPrice) {       matchStage["price.default"] = {};       if (minPrice) matchStage["price.default"].$gte = parseFloat(minPrice);       if (maxPrice) matchStage["price.default"].$lte = parseFloat(maxPrice);     }      const limit = Number(size) || 10;     const pipeline: any[] = [{ $match: matchStage }];      // Sorting logic     if (productsToShow) {       switch (productsToShow) {         case "random":           pipeline.push({ $sample: { size: limit } });           break;         case "best-sellers":           pipeline.push({ $match: { stockQuantity: { $gt: 0 } } });           pipeline.push({ $sort: { stockQuantity: -1 } });           break;         case "expensive":           pipeline.push({ $sort: { "price.default": -1 } });           break;         case "discounted":           pipeline.push({ $match: { discount: { $gt: 0 } } });           break;         default:           break;       }     } else {       let sortOrder: any = {};       switch (sort) {         case "stock-level":           sortOrder.stockQuantity = -1;           break;         case "low-to-high":           sortOrder["price.default"] = 1;           break;         case "high-to-low":           sortOrder["price.default"] = -1;           break;         default:           sortOrder.createdAt = -1;       }       pipeline.push({ $sort: sortOrder });     }      pipeline.push({ $limit: limit });      // Execute main query     const products = await ProductModel.aggregate(pipeline);      // Get total products count     const totalProducts = await ProductModel.countDocuments({ storeId });      // Get all unique colors     const allColors = await ProductModel.aggregate([       { $match: { storeId } },       { $unwind: "$availableColors" },       {         $group: {           _id: null,           colors: { $addToSet: "$availableColors" },         },       },     ]);      // Get all unique sizes     const allSizes = await ProductModel.aggregate([       { $match: { storeId } },       { $unwind: "$availableSizes" },       {         $group: {           _id: null,           sizes: { $addToSet: "$availableSizes" },         },       },     ]);      // Get price range     const priceStats = await ProductModel.aggregate([       { $match: { storeId } },       {         $group: {           _id: null,           minPrice: { $min: "$price.default" },           maxPrice: { $max: "$price.default" },         },       },     ]);      // Get ratings distribution     const ratingsDistribution = await ProductModel.aggregate([       { $match: { storeId } },       {         $group: {           _id: { $floor: "$ratings.average" },           count: { $sum: 1 },         },       },       { $sort: { _id: -1 } },     ]);      // Get metrics for authenticated users     let productsMatricsResponse = undefined;     if (sId) {       const digitalProducts = await ProductModel.countDocuments({         storeId,         isDigital: true,       });       const lowStockProducts = await ProductModel.countDocuments({         storeId,         stockQuantity: { $gt: 0, $lt: 10 },       });       const outOfStockProducts = await ProductModel.countDocuments({         storeId,         stockQuantity: 0,       });        productsMatricsResponse = {         digitalProducts,         lowStockProducts,         outOfStockProducts,       };     }      const { minPrice: storeMinPrice, maxPrice: storeMaxPrice } =       priceStats[0] || { minPrice: 0, maxPrice: 0 };      // Construct response     const response = httpStatusResponse(200, undefined, {       totalProducts,       products,       filters: {         priceRange: { min: storeMinPrice, max: storeMaxPrice },         allColors: allColors[0]?.colors || [],         allSizes: allSizes[0]?.sizes || [],         ratingsDistribution: ratingsDistribution.reduce((acc, curr) => {           acc[curr._id] = curr.count;           return acc;         }, {} as Record<number, number>),       },       ...productsMatricsResponse,     });      return res.status(200).json(response);   } catch (error) {     const err = error as Error;     return res.status(500).json(httpStatusResponse(500, err.message));   } };  There are some mistakes here please fix like $in needs an array and all  
Prompt 2: Without using aggregate
Prompt 3: Get random data from model mongoose
Prompt 4: How to add hover in jsx using style like in styling 
Prompt 5: Without looping! 
Prompt 6: Like get 5 random data
Prompt 7: Was Dexter caught? 
Prompt 8: Do u know about the series Dexter? 
Prompt 9: When Lila went to find agent doakes where he was held captive by Dexter what happen next
Prompt 10: When Lila went to find agent doakes where he was held captive by Dexter what happen next
Comments: No